Source code for immuneML.ml_methods.util.Util

import logging
from datetime import datetime

import numpy as np
import pkg_resources
import torch
from sklearn.preprocessing import label_binarize

from immuneML.environment.Constants import Constants

[docs] class Util:
[docs] @staticmethod def map_to_old_class_values(y, class_mapping: dict): try: old_class_type = np.array(list(class_mapping.values())).dtype mapped_y = np.copy(y).astype(object) for i in range(mapped_y.shape[0]): mapped_y[i] = class_mapping[y[i]] return mapped_y.astype(old_class_type) except Exception as e: logging.exception("MLMethod util: error occurred when predicting the class assignment due to mismatch of class types.\n" f"Classes: {y}\nMapping:{class_mapping}") raise e
[docs] @staticmethod def map_to_new_class_values(y, class_mapping: dict): try: mapped_y = np.copy(y).astype(object) switched_mapping = {value: key for key, value in class_mapping.items()} new_class_type = np.array(list(switched_mapping.values())).dtype for i in range(mapped_y.shape[0]): mapped_y[i] = switched_mapping[y[i]] return mapped_y.astype(new_class_type) except Exception as e: logging.exception(f"MLMethod util: error occurred when fitting the model due to mismatch of class types.\n" f"Classes: {y}\nMapping:{class_mapping}") raise e
[docs] @staticmethod def make_class_mapping(y, positive_class=None) -> dict: """Creates a class mapping from a list of classes which can be strings, numbers of booleans; maps to same name in multi-class settings""" classes = np.unique(y) if classes.shape[0] == 2: return Util.make_binary_class_mapping(y, positive_class) else: return {cls: cls for cls in classes}
[docs] @staticmethod def make_binary_class_mapping(y, positive_class=None) -> dict: """ Creates binary class mapping from a list of classes which can be strings, numbers or boolean values Arguments: y: list of classes per example, as supplied to fit() method of the classifier; it should include all classes that will appear in the data Returns: mapping dictionary where 0 and 1 are always the keys and the values are original class names which were mapped for these values """ unique_values = sorted(set(y)) assert len(unique_values) == 2, f"MLMethod: there has two be exactly two classes to use this classifier," \ f" instead got {str(unique_values)[1:-1]}. For multi-class classification, " \ f"consider some of the other classifiers." if positive_class is None: return {0: unique_values[0], 1: unique_values[1]} else: assert positive_class in unique_values, f"MLMethod: the specified positive class '{positive_class}' does not occur " \ f"in the list of available classes: {str(unique_values)[1:-1]}." unique_values.remove(positive_class) return {0: unique_values[0], 1: positive_class}
[docs] @staticmethod def binarize_label_classes(true_y, predicted_y, classes): """ Binarizes the predictions in place using scikit-learn's label_binarize() method Necessary for some sklearn metrics, like roc_auc_score """ if hasattr(true_y, 'dtype') and (true_y.dtype.type is np.str_ or true_y.dtype.type is np.object_) \ or isinstance(true_y, list) and any(isinstance(item, str) for item in true_y): true_y = label_binarize(true_y, classes=classes) predicted_y = label_binarize(predicted_y, classes=classes) return true_y, predicted_y
[docs] @staticmethod def setup_pytorch(number_of_threads, random_seed, pytorch_device_name=None): torch.set_num_threads(number_of_threads) torch.manual_seed(random_seed) if pytorch_device_name is not None: torch.device(pytorch_device_name)
[docs] @staticmethod def get_immuneML_version(): try: return 'immuneML ' + pkg_resources.get_distribution('immuneML').version except pkg_resources.DistributionNotFound as err: try: return 'immuneML ' + Constants.VERSION except Exception as e: return f'immuneML-dev-{}'